1. Field of the Invention
This invention relates to neural networks and in particular to optical neural networks.
2. Related Art
Neural networks are networks modeled on the interconnections of neurons in animals in which each node of the network, corresponding to a neuron, has an output which depends on the total value of inputs to that node, each input being given a weight value.
One example of the neural network is the single layer neural network in which each neural node sums the weighted values of a set of inputs. In such networks, in particular, the number of interconnections between input and output nodes grows rapidly as the number of output nodes increases. As pointed out by Mitsubishi Electric Corporation in their article in JETRO, Mar. 1989 entitled "Optical Neurochip Developed" the minimum number of nodes for a commercially realisable neural network is of the order of 1000 which requires 1 million interconnections, a number with which existing LSI circuit technologies cannot cope.
The approach of Mitsubishi was to interconnect the input and output nodes by means of optical beams. They constructed an optical neural network in which a single neuron was represented by a line detector which provided a summed output of light inputs as the input for a threshold comparator, a column of line optical sources in the form of light omitting diodes provide inputs to the neuron, and an optical mask between the line sources and line detectors provides a light intensity from a particular diode corresponding to its weight value in the neural net. The remaining output nodes were provided by further rows of optical detectors and associated threshold detectors positioned to receive optical input from the columns of light emitting diodes via the optical mask. Whether a particular input optical beam impinged on a photodetector was determined by switching the light emitting diode on or off.